EMO-5: A high-resolution multi-variable gridded meteorological data set for Europe

Abstract. In this paper we present EMO-51, a European high-resolution, (sub-)daily, multi-variable meteorological data set built on historical and real-time observations obtained by integrating data from 18,964 ground weather stations, four high-resolution regional observational grids (i.e. CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis (ERA-Interim/Land). EMO-5 includes at daily resolution: total precipitation, temperatures (mean, minimum and maximum), wind speed, solar radiation and water vapour pressure. In addition, EMO-5 also makes available 6-hourly precipitation and mean temperature. The raw observations from the ground weather stations underwent a set of quality controls, before SPHEREMAP and Yamamoto interpolation methods were applied in order to estimate for each 5 x 5 km grid cell the variable value and its affiliated uncertainty, respectively. The quality of the EMO-5 precipitation data was evaluated through (1) comparison with two regional high resolution data sets (i.e. seNorge2 and seNorge2018), (2) analysis of 15 heavy precipitation events, and (3) examination of the interpolation uncertainty. Results show that EMO-5 successfully captured 80 % of the heavy precipitation events, and that it is of comparable quality to a regional high resolution data set. The availability of the uncertainty fields increases the transparency of the data set and hence the possible usage. EMO-5 (release 1) covers the time period from 1990 to 2019, with a near real-time release of the latest gridded observations foreseen soon. As a product of Copernicus, the EU's Earth observation programme, EMO-5 dataset is free and open, and can be accessed at https://doi.org/10.2905/0BD84BE4-CEC8-4180-97A6-8B3ADAAC4D26 (Thiemig et al., 2021).1 EMO stands for “European Meteorological Observations”, whereas the 5 denotes the spatial resolution of 5 km.

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A Global High‐Resolution Data Set of Soil Hydraulic and Thermal Properties for Land Surface Modeling

Journal of Advances in Modeling Earth Systems ◽

10.1029/2019ms001784 ◽

2019 ◽

Vol 11 (9) ◽

pp. 2996-3023 ◽

Cited By ~ 4

Author(s):

Yongjiu Dai ◽

Qinchuan Xin ◽

Nan Wei ◽

Yonggen Zhang ◽

Wei Shangguan ◽

...

Keyword(s):

Thermal Properties ◽

High Resolution ◽

Land Surface ◽

Surface Modeling ◽

Data Set ◽

High Resolution Data ◽

Land Surface Modeling ◽

Soil Hydraulic ◽

Resolution Data

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EMO-5: Copernicus pan-European high-resolution meteorological data set for large-scale hydrological modelling

10.5194/egusphere-egu2020-21551 ◽

2020 ◽

Author(s):

Vera Thiemig ◽

Peter Salamon ◽

Goncalo N. Gomes ◽

Jon O. Skøien ◽

Markus Ziese ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Large Scale ◽

Interpolation Method ◽

Meteorological Data ◽

Hydrological Modelling ◽

Data Set ◽

Average Temperature ◽

Time Period ◽

Meteorological Observations

We present EMO-5, a Pan-European high-resolution (5 km), (sub-)daily, multi-variable meteorological data set especially developed to the needs of an operational, pan-European hydrological service (EFAS; European Flood Awareness System). The data set is built on historic and real-time observations coming from 18,964 meteorological in-situ stations, collected from 24 data providers, and 10,632 virtual stations from four high-resolution regional observational grids (CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis product (ERA-Interim-land). This multi-variable data set covers precipitation, temperature (average, min and max), wind speed, solar radiation and vapor pressure; all at daily resolution and in addition 6-hourly resolution for precipitation and average temperature. The original observations were thoroughly quality controlled before we used the Spheremap interpolation method to estimate the variable values for each of the 5 x 5 km grid cells and their affiliated uncertainty. EMO-5 v1 grids covering the time period from 1990 till 2019 will be released as a free and open Copernicus product mid-2020 (with a near real-time release of the latest gridded observations in future). We would like to present the great potential EMO-5 holds for the hydrological modelling community.&#160;footnote: EMO = European Meteorological Observations

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Comments on: A global high-resolution data set of ice sheet topography, cavity geometry and ocean bathymetry

10.5194/essd-2016-3-rc2 ◽

2016 ◽

Author(s):

Martin Jakobsson

Keyword(s):

High Resolution ◽

Ice Sheet ◽

Data Set ◽

High Resolution Data ◽

Resolution Data ◽

Ice Sheet Topography

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HRSC-A data: a new high-resolution data set with multipurpose applications in physical geography

Progress in Physical Geography Earth and Environment ◽

10.1177/0309133307076479 ◽

2007 ◽

Vol 31 (2) ◽

pp. 179-197 ◽

Cited By ~ 12

Author(s):

J.-C. Otto ◽

K. Kleinod ◽

O. König ◽

M. Krautblatter ◽

M. Nyenhuis ◽

...

Keyword(s):

High Resolution ◽

Land Surface ◽

Research Training ◽

Spatial Scales ◽

Three Dimensional ◽

Physical Geography ◽

Vegetation Monitoring ◽

Data Set ◽

High Resolution Data ◽

Resolution Data

The analysis and interpretation of remote sensing data facilitates investigation of land surface complexity on large spatial scales. We introduce here a geometrically high-resolution data set provided by the airborne High Resolution Stereo Camera (HRSC-A). The sensor records digital multispectral and panchromatic stereo bands from which a very high-resolution ground elevation model can be produced. After introducing the basic principles of the HRSC technique and data, applications of HRSC data within the multidisciplinary Research Training Group 437 are presented. Applications include geomorphologic mapping, geomorphometric analysis, mapping of surficial grain-size distribution, rock glacier kinematic analysis, vegetation monitoring and three-dimensional landform visualization. A final evaluation of the HRSC data based on three years of multipurpose usage concludes this presentation. A combination of image and elevation data opens up various possibilities for visualization and three-dimensional analysis of the land surface, especially in geomorphology. Additionally, the multispectral imagery of the HRSC data has potential for land cover mapping and vegetation monitoring. We consider HRSC data a valuable source of high-resolution terrain information with high applicability in physical geography and earth system science.

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The influence of resolution on scale-dependent clustering in fracture spacing data

Interpretation ◽

10.1190/int-2015-0179.1 ◽

2016 ◽

Vol 4 (3) ◽

pp. T387-T394 ◽

Cited By ~ 1

Author(s):

Ankur Roy ◽

Atilla Aydin ◽

Tapan Mukerji

Keyword(s):

High Resolution ◽

Spatial Data ◽

Reservoir Characterization ◽

Data Capture ◽

Low Resolution ◽

Data Set ◽

Bootstrap Analysis ◽

High Resolution Data ◽

Fracture Spacing ◽

Resolution Data

It is a common practice to analyze fracture spacing data collected from scanlines and wells at various resolutions for the purposes of aquifer and reservoir characterization. However, the influence of resolution on such analyses is not well-studied. Lacunarity is a parameter that is used for multiscale analysis of spatial data. In quantitative terms, at any given scale, it is a function of the mean and variance of the distribution of masses captured by a gliding a window of that scale (size) across any pattern of interest. We have described the application of lacunarity for delineating differences between scale-dependent clustering attributes of data collected at different resolutions along a scanline. Specifically, we considered data collected at different resolutions from two outcrop exposures, a pavement and a cliff section, of the Cretaceous turbititic sandstones of the Chatsworth Formation widely exposed in southern California. For each scanline, we analyzed data from low-resolution aerial or ground photographs and high-resolution ground measurements for scale-dependent clustering attributes. High-resolution data show larger values of scale-dependent lacunarity than their respective low-resolution counterparts. We further performed a bootstrap analysis for each data set to test for the significance of such clustering differences. We started with generating 300 realizations for each data set and then ran lacunarity analysis on them. It was seen that lacunarity for higher resolution data set lay significantly outside the upper 90th percentile values, thus proving that higher resolution data are distinctly different from random and fractures are clustered. We have therefore postulated that lower resolution data capture fracture zones that had relatively uniform spacing, whereas higher resolution data capture thin and short splay joints and sheared joints that contribute to fracture clustering. Such findings have important implications in terms of understanding organization of fractures in fracture corridors, which in turn is critical for modeling and upscaling exercises.

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MIRCA2000-Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling

Global Biogeochemical Cycles ◽

10.1029/2008gb003435 ◽

2010 ◽

Vol 24 (1) ◽

pp. n/a-n/a ◽

Cited By ~ 665

Author(s):

Felix T. Portmann ◽

Stefan Siebert ◽

Petra Döll

Keyword(s):

High Resolution ◽

Hydrological Modeling ◽

Data Set ◽

High Resolution Data ◽

Year 2000 ◽

Resolution Data

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Electric vehicle charging stations in the workplace with high-resolution data from casual and habitual users

Scientific Data ◽

10.1038/s41597-021-00956-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Omar Isaac Asensio ◽

M. Cade Lawson ◽

Camila Z. Apablaza

Keyword(s):

High Resolution ◽

Real Time ◽

Electric Vehicle ◽

Optimal Power Flow ◽

Service Providers ◽

High Temporal Resolution ◽

High Resolution Data ◽

Demand Models ◽

Charging Stations ◽

Resolution Data

AbstractProblems of poor network interoperability in electric vehicle (EV) infrastructure, where data about real-time usage or consumption is not easily shared across service providers, has plagued the widespread analysis of energy used for transportation. In this article, we present a high-resolution dataset of real-time EV charging transactions resolved to the nearest second over a one-year period at a multi-site corporate campus. This includes 105 charging stations across 25 different facilities operated by a single firm in the U.S. Department of Energy Workplace Charging Challenge. The high-resolution data has 3,395 real-time transactions and 85 users with both paid and free sessions. The data has been expanded for re-use such as identifying charging behaviour and segmenting user groups by frequency of usage, stage of adoption, and employee type. Potential applications include but are not limited to simulating and parameterizing energy demand models; investigating flexible charge scheduling and optimal power flow problems; characterizing transportation emissions and electric mobility patterns at high temporal resolution; and evaluating characteristics of early adopters and lead user innovation.

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New Data Buoys Watch Typhoons from Within the Storm

Eos ◽

10.1029/2017eo069821 ◽

2017 ◽

Cited By ~ 2

Author(s):

Sen Jan ◽

Yiing Yang ◽

Hung-I Chang ◽

Ming-Huei Chang ◽

Ching-Ling Wei

Keyword(s):

High Resolution ◽

Numerical Model ◽

Pacific Ocean ◽

Real Time ◽

Northwestern Pacific ◽

Northwestern Pacific Ocean ◽

Time Data ◽

High Resolution Data ◽

Real Time Data ◽

Resolution Data

Advanced real-time data buoys have observed nine strong typhoons in the northwestern Pacific Ocean since 2015, providing high-resolution data and reducing the uncertainty of numerical model forecasts.

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Crambin - record in crystallographic resolution for a biological macromolecule

Acta Crystallographica Section A Foundations and Advances ◽

10.1107/s205327331408797x ◽

2014 ◽

Vol 70 (a1) ◽

pp. C1202-C1202

Author(s):

Johanna Kallio ◽

Victor Lamzin

Keyword(s):

High Resolution ◽

Data Interpretation ◽

High Energy ◽

Structural Features ◽

Biological Macromolecule ◽

Data Set ◽

Synchrotron Source ◽

Small Protein ◽

High Resolution Data ◽

Resolution Data

A couple of years ago a study was presented that set the record for the highest X-ray crystallographic resolution for a biological macromolecule [1]. The structure of the small protein crambin was determined to 0.48 Å resolution - this almost doubled the amount of available experimental data. Crambin is a small protein consisting of 46 amino acids belonging to the thionin family. Although the protein and its structure has long been known, it lacks any obvious enzymatic activity and has a hard-to-guess biological function. The protein crystallizes readily and serves as an excellent specimen for exploring the limits of resolution of the diffraction. The results demonstrated the possibilities that can be offered by a high-energy synchrotron source. The structure refined with Refmac, Shelxl and Mopro revealed a wealth of details. Bonding electron density became visible along the main chain. However, no fundamental additional structural features could be detected in comparison to the previously collected data set to 0.54 Å resolution. The availability of extremely high-resolution data is certainly of great help to drive further software development and methods for data interpretation. The question will always remain as to what the true limits are in terms of what can be seen in a biological macromolecule. Here we will present the results of our recent efforts in interpretation of such ultra-high resolution data.

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